The solution for multi-objective flexible job shop scheduling based on genetic algorithm

This paper provides a meaningful comprehensive goal for the flexible job shop scheduling – make the possible reduction of the machine burden while we shorten the span of manufacturing. Because the traditional genetic algorithm has localizations in the solution to flexible job shop scheduling, we propose an improved genetic algorithm. Firstly, we give the algorithm of scheduling according to the competitive objective in order to obtain initial solutions. Secondly, we add the coding method based on machine assignment to the general coding method based on procedure order and design the corresponding crossover, mutation operations according to the characteristic of flexible job shop scheduling. Finally, we design the migration operation to solve multi-objective optimization problems according to the idea of environment migration in the phenomenon of species evolution. The result of experiment shows than the improved genetic algorithm is superior to the traditional one in the solution to multi-objective flexible job shop scheduling.